An Improved Approach for Cardiac MRI Segmentation based on 3D UNet Combined with Papillary Muscle Exclusion
Narjes Benameur, Ramzi Mahmoudi, Mohamed Deriche, Amira fayouka, Imene, Masmoudi, Nessrine Zoghlami

TL;DR
This paper presents an improved 3D UNet model for cardiac MRI segmentation that accurately excludes papillary muscles, enhancing the precision of left ventricular function assessment.
Contribution
The study introduces a novel 3D UNet-based segmentation method that specifically excludes papillary muscles, improving clinical measurement accuracy.
Findings
Achieved Dice index of 0.965 at end diastole
Achieved Dice index of 0.945 at end systole
Significant difference in LVEF when papillary muscles are excluded
Abstract
Left ventricular ejection fraction (LVEF) is the most important clinical parameter of cardiovascular function. The accuracy in estimating this parameter is highly dependent upon the precise segmentation of the left ventricle (LV) structure at the end diastole and systole phases. Therefore, it is crucial to develop robust algorithms for the precise segmentation of the heart structure during different phases. Methodology: In this work, an improved 3D UNet model is introduced to segment the myocardium and LV, while excluding papillary muscles, as per the recommendation of the Society for Cardiovascular Magnetic Resonance. For the practical testing of the proposed framework, a total of 8,400 cardiac MRI images were collected and analysed from the military hospital in Tunis (HMPIT), as well as the popular ACDC public dataset. As performance metrics, we used the Dice coefficient and the F1…
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Taxonomy
TopicsRadiomics and Machine Learning in Medical Imaging · Advanced X-ray and CT Imaging · Medical Image Segmentation Techniques
